SPDF: A Schedulable Parametric Data-Flow MoC (Extended Version)

Pascal Fradet 1 Alain Girault 1 Peter Poplavko 1
1 POP ART - Programming languages, Operating Systems, Parallelism, and Aspects for Real-Time
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : Dataflow programming models are suitable to express multi-core streaming applications. The design of high-quality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model of computation. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study.
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[Research Report] RR-7828, INRIA. 2011, pp.24
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Pascal Fradet, Alain Girault, Peter Poplavko. SPDF: A Schedulable Parametric Data-Flow MoC (Extended Version). [Research Report] RR-7828, INRIA. 2011, pp.24. 〈hal-00666284〉

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